
"""simple docstring"""


class 	__a	:






       def __init__(	self			:				Any							,   UpperCAmelCase_			:				Tuple							,   UpperCAmelCase_			:				Optional[int]							,   UpperCAmelCase_			:				List[Any]      )->	Union[str, Any]:







              """simple docstring"""
              UpperCamelCase       			=		None
              UpperCamelCase       			=		None
              UpperCamelCase       			=		graph

              self._normalize_graph(UpperCAmelCase_							,   UpperCAmelCase_      )
              UpperCamelCase       			=		len(UpperCAmelCase_      )
              UpperCamelCase       			=		None






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any							,   UpperCAmelCase_			:				Tuple							,   UpperCAmelCase_			:				Tuple      )->	Optional[int]:







              """simple docstring"""
              if sources is int:
                     UpperCamelCase       			=		[sources]
              if sinks is int:
                     UpperCamelCase       			=		[sinks]

              if len(UpperCAmelCase_      ) == 0 or len(UpperCAmelCase_      ) == 0:
                     return

              UpperCamelCase       			=		sources[0]
              UpperCamelCase       			=		sinks[0]

              # make fake vertex if there are more
              # than one source or sink
              if len(UpperCAmelCase_      ) > 1 or len(UpperCAmelCase_      ) > 1:
                     UpperCamelCase       			=		0
                     for i in sources:
                            max_input_flow += sum(self.graph[i]      )

                     UpperCamelCase       			=		len(self.graph      ) + 1
                     for room in self.graph:
                            room.insert(0							,   0      )
                     self.graph.insert(0							,   [0] * size      )
                     for i in sources:
                            UpperCamelCase       			=		max_input_flow
                     UpperCamelCase       			=		0

                     UpperCamelCase       			=		len(self.graph      ) + 1
                     for room in self.graph:
                            room.append(0      )
                     self.graph.append([0] * size      )
                     for i in sinks:
                            UpperCamelCase       			=		max_input_flow
                     UpperCamelCase       			=		size - 1






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Tuple      )->	int:







              """simple docstring"""
              if self.maximum_flow_algorithm is None:
                     raise Exception("You need to set maximum flow algorithm before."      )
              if self.source_index is None or self.sink_index is None:
                     return 0

              self.maximum_flow_algorithm.execute()
              return self.maximum_flow_algorithm.getMaximumFlow()






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]							,   UpperCAmelCase_			:				Optional[Any]      )->	Tuple:







              """simple docstring"""
              UpperCamelCase       			=		algorithm(self      )





class 	__a	:






       def __init__(	self			:				int							,   UpperCAmelCase_			:				int      )->	int:







              """simple docstring"""
              UpperCamelCase       			=		flow_network
              UpperCamelCase       			=		flow_network.verticesCount
              UpperCamelCase       			=		flow_network.sourceIndex
              UpperCamelCase       			=		flow_network.sinkIndex
              # it's just a reference, so you shouldn't change
              # it in your algorithms, use deep copy before doing that
              UpperCamelCase       			=		flow_network.graph
              UpperCamelCase       			=		False






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]      )->	str:







              """simple docstring"""
              if not self.executed:
                     self._algorithm()
                     UpperCamelCase       			=		True






       def 						_SCREAMING_SNAKE_CASE							(	self			:				str      )->	Optional[int]:







              """simple docstring"""
              pass





class 	__a	(   _lowerCAmelCase						):






       def __init__(	self			:				str							,   UpperCAmelCase_			:				str      )->	List[str]:







              """simple docstring"""
              super().__init__(UpperCAmelCase_      )
              # use this to save your result
              UpperCamelCase       			=		-1






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[Any]      )->	str:







              """simple docstring"""
              if not self.executed:
                     raise Exception("You should execute algorithm before using its result!"      )

              return self.maximum_flow





class 	__a	(   _lowerCAmelCase						):






       def __init__(	self			:				Any							,   UpperCAmelCase_			:				int      )->	Any:







              """simple docstring"""
              super().__init__(UpperCAmelCase_      )

              UpperCamelCase       			=		[[0] * self.verticies_count for i in range(self.verticies_count      )]

              UpperCamelCase       			=		[0] * self.verticies_count
              UpperCamelCase       			=		[0] * self.verticies_count






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]      )->	Union[str, Any]:







              """simple docstring"""
              UpperCamelCase       			=		self.verticies_count

              # push some substance to graph
              for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index]      ):
                     self.preflow[self.source_index][nextvertex_index] += bandwidth
                     self.preflow[nextvertex_index][self.source_index] -= bandwidth
                     self.excesses[nextvertex_index] += bandwidth

              # Relabel-to-front selection rule
              UpperCamelCase       			=		[
                  i
                  for i in range(self.verticies_count      )
                  if i != self.source_index and i != self.sink_index
              ]

              # move through list
              UpperCamelCase       			=		0
              while i < len(UpperCAmelCase_      ):
                     UpperCamelCase       			=		vertices_list[i]
                     UpperCamelCase       			=		self.heights[vertex_index]
                     self.process_vertex(UpperCAmelCase_      )
                     if self.heights[vertex_index] > previous_height:
                            # if it was relabeled, swap elements
                            # and start from 0 index
                            vertices_list.insert(0							,   vertices_list.pop(UpperCAmelCase_      )      )
                            UpperCamelCase       			=		0
                     else:
                            i += 1

              UpperCamelCase       			=		sum(self.preflow[self.source_index]      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				int							,   UpperCAmelCase_			:				List[str]      )->	Optional[int]:







              """simple docstring"""
              while self.excesses[vertex_index] > 0:
                     for neighbour_index in range(self.verticies_count      ):
                            # if it's neighbour and current vertex is higher
                            if (
                                self.graph[vertex_index][neighbour_index]
                                - self.preflow[vertex_index][neighbour_index]
                                > 0
                                and self.heights[vertex_index] > self.heights[neighbour_index]
                            ):
                                   self.push(UpperCAmelCase_							,   UpperCAmelCase_      )

                     self.relabel(UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]							,   UpperCAmelCase_			:				List[Any]							,   UpperCAmelCase_			:				Tuple      )->	str:







              """simple docstring"""
              UpperCamelCase       			=		min(
                  self.excesses[from_index]							,   self.graph[from_index][to_index] - self.preflow[from_index][to_index]							,   )
              self.preflow[from_index][to_index] += preflow_delta
              self.preflow[to_index][from_index] -= preflow_delta
              self.excesses[from_index] -= preflow_delta
              self.excesses[to_index] += preflow_delta






       def 						_SCREAMING_SNAKE_CASE							(	self			:				int							,   UpperCAmelCase_			:				Any      )->	Tuple:







              """simple docstring"""
              UpperCamelCase       			=		None
              for to_index in range(self.verticies_count      ):
                     if (
                         self.graph[vertex_index][to_index]
                         - self.preflow[vertex_index][to_index]
                         > 0
                     ) and (min_height is None or self.heights[to_index] < min_height):
                            UpperCamelCase       			=		self.heights[to_index]

              if min_height is not None:
                     UpperCamelCase       			=		min_height + 1


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE			  =  [0]
      SCREAMING_SNAKE_CASE			  =  [3]
      # graph = [
      #     [0, 0, 4, 6, 0, 0],
      #     [0, 0, 5, 2, 0, 0],
      #     [0, 0, 0, 0, 4, 4],
      #     [0, 0, 0, 0, 6, 6],
      #     [0, 0, 0, 0, 0, 0],
      #     [0, 0, 0, 0, 0, 0],
      # ]
      SCREAMING_SNAKE_CASE			  =  [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

      # prepare our network
      SCREAMING_SNAKE_CASE			  =  FlowNetwork(graph, entrances, exits)
      # set algorithm
      flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
      # and calculate
      SCREAMING_SNAKE_CASE			  =  flow_network.find_maximum_flow()

      print(F'''maximum flow is {maximum_flow}''')







"""simple docstring"""


import inspect
import os
import re

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
SCREAMING_SNAKE_CASE			  =  """src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
SCREAMING_SNAKE_CASE			  =  direct_transformers_import(PATH_TO_TRANSFORMERS)

SCREAMING_SNAKE_CASE			  =  transformers.models.auto.configuration_auto.CONFIG_MAPPING

SCREAMING_SNAKE_CASE			  =  {
    # used to compute the property `self.chunk_length`
    """EncodecConfig""": ["""overlap"""],
    # used as `self.bert_model = BertModel(config, ...)`
    """DPRConfig""": True,
    # not used in modeling files, but it's an important information
    """FSMTConfig""": ["""langs"""],
    # used internally in the configuration class file
    """GPTNeoConfig""": ["""attention_types"""],
    # used internally in the configuration class file
    """EsmConfig""": ["""is_folding_model"""],
    # used during training (despite we don't have training script for these models yet)
    """Mask2FormerConfig""": ["""ignore_value"""],
    # `ignore_value` used during training (despite we don't have training script for these models yet)
    # `norm` used in conversion script (despite not using in the modeling file)
    """OneFormerConfig""": ["""ignore_value""", """norm"""],
    # used during preprocessing and collation, see `collating_graphormer.py`
    """GraphormerConfig""": ["""spatial_pos_max"""],
    # used internally in the configuration class file
    """T5Config""": ["""feed_forward_proj"""],
    # used internally in the configuration class file
    # `tokenizer_class` get default value `T5Tokenizer` intentionally
    """MT5Config""": ["""feed_forward_proj""", """tokenizer_class"""],
    """UMT5Config""": ["""feed_forward_proj""", """tokenizer_class"""],
    # used internally in the configuration class file
    """LongT5Config""": ["""feed_forward_proj"""],
    # used internally in the configuration class file
    """SwitchTransformersConfig""": ["""feed_forward_proj"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """BioGptConfig""": ["""layer_norm_eps"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """GLPNConfig""": ["""layer_norm_eps"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """SegformerConfig""": ["""layer_norm_eps"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """CvtConfig""": ["""layer_norm_eps"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """PerceiverConfig""": ["""layer_norm_eps"""],
    # used internally to calculate the feature size
    """InformerConfig""": ["""num_static_real_features""", """num_time_features"""],
    # used internally to calculate the feature size
    """TimeSeriesTransformerConfig""": ["""num_static_real_features""", """num_time_features"""],
    # used internally to calculate the feature size
    """AutoformerConfig""": ["""num_static_real_features""", """num_time_features"""],
    # used internally to calculate `mlp_dim`
    """SamVisionConfig""": ["""mlp_ratio"""],
    # For (head) training, but so far not implemented
    """ClapAudioConfig""": ["""num_classes"""],
    # Not used, but providing useful information to users
    """SpeechT5HifiGanConfig""": ["""sampling_rate"""],
}


# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
    {
        """CLIPSegConfig""": True,
        """DeformableDetrConfig""": True,
        """DetaConfig""": True,
        """DinatConfig""": True,
        """DonutSwinConfig""": True,
        """EfficientFormerConfig""": True,
        """FSMTConfig""": True,
        """JukeboxConfig""": True,
        """LayoutLMv2Config""": True,
        """MaskFormerSwinConfig""": True,
        """MT5Config""": True,
        """NatConfig""": True,
        """OneFormerConfig""": True,
        """PerceiverConfig""": True,
        """RagConfig""": True,
        """SpeechT5Config""": True,
        """SwinConfig""": True,
        """Swin2SRConfig""": True,
        """Swinv2Config""": True,
        """SwitchTransformersConfig""": True,
        """TableTransformerConfig""": True,
        """TapasConfig""": True,
        """TransfoXLConfig""": True,
        """UniSpeechConfig""": True,
        """UniSpeechSatConfig""": True,
        """WavLMConfig""": True,
        """WhisperConfig""": True,
        # TODO: @Arthur (for `alignment_head` and `alignment_layer`)
        """JukeboxPriorConfig""": True,
        # TODO: @Younes (for `is_decoder`)
        """Pix2StructTextConfig""": True,
    }
)



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				Optional[Any]:







       """simple docstring"""
       UpperCamelCase       			=		False
       for attribute in attributes:
              for modeling_source in source_strings:
                     # check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
                     if (
                         F"config.{attribute}" in modeling_source
                         or F"getattr(config, \"{attribute}\"" in modeling_source
                         or F"getattr(self.config, \"{attribute}\"" in modeling_source
                     ):
                            UpperCamelCase       			=		True
                     # Deal with multi-line cases
                     elif (
                         re.search(
                             RF"getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*\"{attribute}\""		,   UpperCAmelCase_		,   )
                         is not None
                     ):
                            UpperCamelCase       			=		True
                     # `SequenceSummary` is called with `SequenceSummary(config)`
                     elif attribute in [
                         "summary_type",
                         "summary_use_proj",
                         "summary_activation",
                         "summary_last_dropout",
                         "summary_proj_to_labels",
                         "summary_first_dropout",
                     ]:
                            if "SequenceSummary" in modeling_source:
                                   UpperCamelCase       			=		True
                     if attribute_used:
                            break
              if attribute_used:
                     break

    # common and important attributes, even if they do not always appear in the modeling files
       UpperCamelCase       			=		[
           "bos_index",
           "eos_index",
           "pad_index",
           "unk_index",
           "mask_index",
           "image_size",
           "use_cache",
           "out_features",
           "out_indices",
       ]
       UpperCamelCase       			=		["encoder_no_repeat_ngram_size"]

       # Special cases to be allowed
       UpperCamelCase       			=		True
       if not attribute_used:
              UpperCamelCase       			=		False
              for attribute in attributes:
                     # Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
                     if attribute in ["is_encoder_decoder"] and default_value is True:
                            UpperCamelCase       			=		True
                     elif attribute in ["tie_word_embeddings"] and default_value is False:
                            UpperCamelCase       			=		True

                     # Allow cases without checking the default value in the configuration class
                     elif attribute in attributes_to_allow + attributes_used_in_generation:
                            UpperCamelCase       			=		True
                     elif attribute.endswith("_token_id"					):
                            UpperCamelCase       			=		True

                     # configuration class specific cases
                     if not case_allowed:
                            UpperCamelCase       			=		SPECIAL_CASES_TO_ALLOW.get(config_class.__name__		,   []					)
                            UpperCamelCase       			=		allowed_cases is True or attribute in allowed_cases

       return attribute_used or case_allowed



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				Optional[Any]:







       """simple docstring"""
       UpperCamelCase       			=		dict(inspect.signature(config_class.__init__					).parameters					)
       UpperCamelCase       			=		[x for x in list(signature.keys()					) if x not in ["self", "kwargs"]]
       UpperCamelCase       			=		[signature[param].default for param in parameter_names]

       # If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
       # as one variant is used, the test should pass
       UpperCamelCase       			=		{}
       if len(config_class.attribute_map					) > 0:
              UpperCamelCase       			=		{v: k for k, v in config_class.attribute_map.items()}

       # Get the path to modeling source files
       UpperCamelCase       			=		inspect.getsourcefile(UpperCAmelCase_					)
       UpperCamelCase       			=		os.path.dirname(UpperCAmelCase_					)
       # Let's check against all frameworks: as long as one framework uses an attribute, we are good.
       UpperCamelCase       			=		[os.path.join(UpperCAmelCase_		,   UpperCAmelCase_					) for fn in os.listdir(UpperCAmelCase_					) if fn.startswith("modeling_"					)]

       # Get the source code strings
       UpperCamelCase       			=		[]
       for path in modeling_paths:
              if os.path.isfile(UpperCAmelCase_					):
                     with open(UpperCAmelCase_					) as fp:
                            modeling_sources.append(fp.read()					)

       UpperCamelCase       			=		[]
       for config_param, default_value in zip(UpperCAmelCase_		,   UpperCAmelCase_					):
              # `attributes` here is all the variant names for `config_param`
              UpperCamelCase       			=		[config_param]
              # some configuration classes have non-empty `attribute_map`, and both names could be used in the
              # corresponding modeling files. As long as one of them appears, it is fine.
              if config_param in reversed_attribute_map:
                     attributes.append(reversed_attribute_map[config_param]					)

              if not check_attribute_being_used(UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					):
                     unused_attributes.append(attributes[0]					)

       return sorted(UpperCAmelCase_					)



def 						lowerCamelCase__      (				)->				List[str]:







       """simple docstring"""
       UpperCamelCase       			=		{}
       for _config_class in list(CONFIG_MAPPING.values()					):
              # Skip deprecated models
              if "models.deprecated" in _config_class.__module__:
                     continue
              # Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
              UpperCamelCase       			=		[
                  cls
                  for name, cls in inspect.getmembers(
                      inspect.getmodule(_config_class					)		,   lambda UpperCAmelCase_					: inspect.isclass(UpperCAmelCase_					)
                      and issubclass(UpperCAmelCase_		,   UpperCAmelCase_					)
                      and inspect.getmodule(UpperCAmelCase_					) == inspect.getmodule(_config_class					)		,   )
              ]
              for config_class in config_classes_in_module:
                     UpperCamelCase       			=		check_config_attributes_being_used(UpperCAmelCase_					)
                     if len(UpperCAmelCase_					) > 0:
                            UpperCamelCase       			=		unused_attributes

       if len(UpperCAmelCase_					) > 0:
              UpperCamelCase       			=		"The following configuration classes contain unused attributes in the corresponding modeling files:\n"
              for name, attributes in configs_with_unused_attributes.items():
                     error += F"{name}: {attributes}\n"

              raise ValueError(UpperCAmelCase_					)


if __name__ == "__main__":
      check_config_attributes()





